func.func private @f(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  "SomeOp"(%arg1) : (tensor<f32>) -> ()
  return %arg1 : tensor<f32>
}

// CHECK-LABEL: removes_first_arg
func.func @removes_first_arg(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  // CHECK: call{{.*}}(%arg1)
  %1 = func.call @f(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  return %1 : tensor<f32>
}

func.func @test_batch_matmul_to_einsum(%arg0: tensor<1x2x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x2x4xf32> {
  // CHECK-LABEL: test_batch_matmul_to_einsum
  // CHECK: "tf.Einsum"(%arg0, %arg1) <{equation = "...mk,...kn->...mn"}> : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {adj_x = false, adj_y = false} : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>

// CHECK-LABEL: listReserveScalarShapeI32
func.func @listReserveScalarShapeI32(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListReserve"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>

CHECK:  %[[LSTM:[a-z0-9]*]] = "tfl.unidirectional_sequence_lstm"(%arg2, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg1, %arg1, %arg1, %arg1, %arg1, %arg1, %arg1, %arg0, %arg1, %[[CST_0]], %[[CST_1]], %arg0, %arg0, %arg0, %arg0) <{fused_activation_function = "NONE", time_major = true}> : (tensor<4x4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>,...

// CHECK-LABEL: bias_add
func.func @bias_add(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>

// CHECK-SAME: (%[[ARG0:.*]]: tensor<i1>, %[[ARG1:.*]]: tensor<4xf32>)
func.func @test_supported_lowering_of_tf_if_region2(%arg0: tensor<i1>, %arg1: tensor<4xf32>) -> () {
  "tf.IfRegion"(%arg0) ({
    func.call @test_if_then2(%arg1) : (tensor<4xf32>) -> ()
    "tf.Yield"() : () -> ()
  },  {
    func.call @test_if_else2(%arg1) : (tensor<4xf32>) -> ()
    "tf.Yield"() : () -> ()

public class AllowedMethodTypesClass {
    public void validMethod(String arg1, String arg2) {

    }

    public File invalidReturnType(String arg1, String arg2) {
        return null;
    }

    public void invalidParameterType(String arg1, File arg2) {

    }

    public <T extends File> void invalidTypeParameterBoundType(String arg1, String arg2) {

    }

func.func @main(%arg0: tensor<4x10x15xf32>, %arg1: tensor<4x15x17xf32>) -> tensor<4x10x17xf32> {
  %0 = "tfl.batch_matmul"(%arg0, %arg1) {adj_x = false, adj_y = false} : (tensor<4x10x15xf32>, tensor<4x15x17xf32>) -> tensor<4x10x17xf32>
  func.return %0:  tensor<4x10x17xf32>

// CHECK-LABEL: main

// CHECK-LABEL: func @device_func_3
// CHECK-NOT: tf.aliasing_output
func.func @device_func_3(%arg0: tensor<f32>, %arg1: tensor<f32>) -> (tensor<f32>, tensor<f32>) {
  func.return %arg1, %arg0 : tensor<f32>, tensor<f32>
}

// CHECK-LABEL: func @skip_multiple_reads_of_resource
func.func @skip_multiple_reads_of_resource(%arg0: !tf_res_f32, %arg1: !tf_res_f32) {
  %0 = "tf.ReadVariableOp"(%arg0) : (!tf_res_f32) -> tensor<f32>

  func.return %1 : tensor<1xf32>
}

func.func @cond_true(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

func.func @cond_false(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = tfl.mul %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<*xf32>
  func.return %0 : tensor<*xf32>